Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations127469
Missing cells1700397
Missing cells (%)46.0%
Duplicate rows11
Duplicate rows (%)< 0.1%
Total size in memory28.2 MiB
Average record size in memory232.0 B

Variable types

Numeric8
DateTime2
Text10
Categorical9

Alerts

Негативная информация has constant value "Имеется" Constant
Негатив относительно ГД has constant value "Имеется" Constant
Dataset has 11 (< 0.1%) duplicate rowsDuplicates
Планируемый оборот по анкете (руб) is highly overall correlated with Кол-во сотрудников and 1 other fieldsHigh correlation
Планируемый оборот по снятию д/с (руб) is highly overall correlated with Кол-во сотрудниковHigh correlation
Деятельность клиента со слов клиента is highly overall correlated with Кол-во сотрудниковHigh correlation
ЗСК is highly overall correlated with Кол-во сотрудниковHigh correlation
Кол-во сотрудников is highly overall correlated with Планируемый оборот по анкете (руб) and 5 other fieldsHigh correlation
Кол-во сотрудников со слов клиента is highly overall correlated with Кол-во сотрудниковHigh correlation
Налоговая нагрузка is highly overall correlated with Планируемый оборот по анкете (руб)High correlation
Система налогообложения is highly overall correlated with Кол-во сотрудниковHigh correlation
Срок жизни SIM в текущем пользовательском устройстве is highly overall correlated with Срок жизни SIM-карты/номера (количество дней/часов/минут, которое прошло от даты заключения договора)High correlation
Срок жизни SIM-карты/номера (количество дней/часов/минут, которое прошло от даты заключения договора) is highly overall correlated with Срок жизни SIM в текущем пользовательском устройствеHigh correlation
Кол-во сотрудников со слов клиента is highly imbalanced (67.1%) Imbalance
Уставной капитал (руб) has 71284 (55.9%) missing values Missing
Адрес has 25063 (19.7%) missing values Missing
ФИО Генерального директора has 53430 (41.9%) missing values Missing
Дата рождения Генерального директора has 53430 (41.9%) missing values Missing
ФИО Бенефициара has 124513 (97.7%) missing values Missing
Кол-во сотрудников has 104595 (82.1%) missing values Missing
Сайт has 124594 (97.7%) missing values Missing
Провайдер has 22042 (17.3%) missing values Missing
Система налогообложения has 53223 (41.8%) missing values Missing
Деятельность клиента has 53131 (41.7%) missing values Missing
Деятельность клиента со слов клиента has 117287 (92.0%) missing values Missing
Кол-во сотрудников со слов клиента has 53130 (41.7%) missing values Missing
Планируемый оборот по анкете (руб) has 60785 (47.7%) missing values Missing
Планируемый оборот по снятию д/с (руб) has 53130 (41.7%) missing values Missing
Доходы (тыс, руб.) has 112147 (88.0%) missing values Missing
ЗСК has 76369 (59.9%) missing values Missing
Негативная информация has 93228 (73.1%) missing values Missing
Негатив относительно ГД has 111296 (87.3%) missing values Missing
Мошенники has 67434 (52.9%) missing values Missing
Сервисы регистраторы has 67434 (52.9%) missing values Missing
Срок жизни SIM-карты/номера (от даты замены e/SIM-карты) has 67434 (52.9%) missing values Missing
Срок жизни SIM в текущем пользовательском устройстве has 67434 (52.9%) missing values Missing
Срок жизни SIM-карты/номера (количество дней/часов/минут, которое прошло от даты заключения договора) has 67434 (52.9%) missing values Missing
Кол-во сотрудников is highly skewed (γ1 = 33.08066983) Skewed
Кол-во сотрудников has 4807 (3.8%) zeros Zeros
Сервисы регистраторы has 24122 (18.9%) zeros Zeros
Срок жизни SIM-карты/номера (от даты замены e/SIM-карты) has 39153 (30.7%) zeros Zeros
Срок жизни SIM в текущем пользовательском устройстве has 41349 (32.4%) zeros Zeros
Срок жизни SIM-карты/номера (количество дней/часов/минут, которое прошло от даты заключения договора) has 40944 (32.1%) zeros Zeros

Reproduction

Analysis started2024-12-20 16:49:17.960581
Analysis finished2024-12-20 16:49:57.858985
Duration39.9 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

ИНН
Real number (ℝ)

Distinct126324
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7627078 × 1011
Minimum1.0000033 × 108
Maximum9.9019961 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 KiB
2024-12-20T16:49:58.111082image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.0000033 × 108
5-th percentile2.2228591 × 109
Q17.7198043 × 109
median1.6520645 × 1011
Q35.64819 × 1011
95-th percentile7.7337721 × 1011
Maximum9.9019961 × 1011
Range9.9009961 × 1011
Interquartile range (IQR)5.570992 × 1011

Descriptive statistics

Standard deviation2.9935861 × 1011
Coefficient of variation (CV)1.0835696
Kurtosis-1.3278651
Mean2.7627078 × 1011
Median Absolute Deviation (MAD)1.6020531 × 1011
Skewness0.53782368
Sum3.5215959 × 1016
Variance8.9615578 × 1022
MonotonicityNot monotonic
2024-12-20T16:49:58.637006image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.200000895 × 10114
 
< 0.1%
7.725767552 × 10114
 
< 0.1%
6.004008596 × 10114
 
< 0.1%
3.525184685 × 10113
 
< 0.1%
7724787709 3
 
< 0.1%
9703056860 3
 
< 0.1%
2.128016022 × 10113
 
< 0.1%
1.610005324 × 10113
 
< 0.1%
3.428996645 × 10113
 
< 0.1%
7736605093 3
 
< 0.1%
Other values (126314) 127436
> 99.9%
ValueCountFrequency (%)
100000332 1
< 0.1%
100001103 1
< 0.1%
100001953 1
< 0.1%
100002210 1
< 0.1%
100002442 1
< 0.1%
100004249 1
< 0.1%
100004665 1
< 0.1%
100005732 1
< 0.1%
100005813 1
< 0.1%
100008282 1
< 0.1%
ValueCountFrequency (%)
9.901996111 × 10111
< 0.1%
9.901990659 × 10111
< 0.1%
9.90103507 × 10111
< 0.1%
9.90103155 × 10111
< 0.1%
9.901031362 × 10111
< 0.1%
9.901014377 × 10111
< 0.1%
9.901013848 × 10111
< 0.1%
9.901003981 × 10111
< 0.1%
9.733066528 × 10111
< 0.1%
9.73305799 × 10111
< 0.1%
Distinct1333
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size996.0 KiB
Minimum2021-04-06 00:00:00
Maximum2024-11-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-20T16:49:59.083242image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:50:00.026067image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1454
Distinct (%)2.6%
Missing71284
Missing (%)55.9%
Memory size996.0 KiB
2024-12-20T16:50:00.820940image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length25
Median length12
Mean length12.174068
Min length6

Characters and Unicode

Total characters684000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1169 ?
Unique (%)2.1%

Sample

1st row 10 000,00
2nd row 100 000,00
3rd row 10 000,00
4th row 50 000,00
5th row 10 000,00
ValueCountFrequency (%)
000,00 54462
48.0%
10 26489
23.4%
50 5089
 
4.5%
100 4217
 
3.7%
20 3484
 
3.1%
30 3363
 
3.0%
15 1480
 
1.3%
25 1022
 
0.9%
40 893
 
0.8%
12 843
 
0.7%
Other values (1049) 12066
 
10.6%
2024-12-20T16:50:01.919882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 332370
48.6%
169771
24.8%
  57223
 
8.4%
, 55577
 
8.1%
1 37682
 
5.5%
5 11241
 
1.6%
2 7604
 
1.1%
3 5092
 
0.7%
4 2093
 
0.3%
6 1647
 
0.2%
Other values (4) 3700
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 684000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 332370
48.6%
169771
24.8%
  57223
 
8.4%
, 55577
 
8.1%
1 37682
 
5.5%
5 11241
 
1.6%
2 7604
 
1.1%
3 5092
 
0.7%
4 2093
 
0.3%
6 1647
 
0.2%
Other values (4) 3700
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 684000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 332370
48.6%
169771
24.8%
  57223
 
8.4%
, 55577
 
8.1%
1 37682
 
5.5%
5 11241
 
1.6%
2 7604
 
1.1%
3 5092
 
0.7%
4 2093
 
0.3%
6 1647
 
0.2%
Other values (4) 3700
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 684000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 332370
48.6%
169771
24.8%
  57223
 
8.4%
, 55577
 
8.1%
1 37682
 
5.5%
5 11241
 
1.6%
2 7604
 
1.1%
3 5092
 
0.7%
4 2093
 
0.3%
6 1647
 
0.2%
Other values (4) 3700
 
0.5%

Адрес
Text

Missing 

Distinct4758
Distinct (%)4.6%
Missing25063
Missing (%)19.7%
Memory size996.0 KiB
2024-12-20T16:50:02.383865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length126
Median length123
Mean length15.211335
Min length6

Characters and Unicode

Total characters1557732
Distinct characters89
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3506 ?
Unique (%)3.4%

Sample

1st rowг. Москва
2nd rowг. Уфа
3rd rowАстраханская область
4th rowг. Москва
5th rowЧелябинская область
ValueCountFrequency (%)
г 83401
33.6%
москва 28441
 
11.4%
область 7998
 
3.2%
обл 7384
 
3.0%
санкт-петербург 7351
 
3.0%
край 3995
 
1.6%
республика 3373
 
1.4%
казань 2990
 
1.2%
челябинск 2971
 
1.2%
екатеринбург 2870
 
1.2%
Other values (4673) 97711
39.3%
2024-12-20T16:50:03.199855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
146657
 
9.4%
а 126036
 
8.1%
о 118201
 
7.6%
с 108016
 
6.9%
г 106463
 
6.8%
к 100154
 
6.4%
р 78711
 
5.1%
. 72229
 
4.6%
в 60380
 
3.9%
е 59163
 
3.8%
Other values (79) 581722
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1557732
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
146657
 
9.4%
а 126036
 
8.1%
о 118201
 
7.6%
с 108016
 
6.9%
г 106463
 
6.8%
к 100154
 
6.4%
р 78711
 
5.1%
. 72229
 
4.6%
в 60380
 
3.9%
е 59163
 
3.8%
Other values (79) 581722
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1557732
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
146657
 
9.4%
а 126036
 
8.1%
о 118201
 
7.6%
с 108016
 
6.9%
г 106463
 
6.8%
к 100154
 
6.4%
р 78711
 
5.1%
. 72229
 
4.6%
в 60380
 
3.9%
е 59163
 
3.8%
Other values (79) 581722
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1557732
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
146657
 
9.4%
а 126036
 
8.1%
о 118201
 
7.6%
с 108016
 
6.9%
г 106463
 
6.8%
к 100154
 
6.4%
р 78711
 
5.1%
. 72229
 
4.6%
в 60380
 
3.9%
е 59163
 
3.8%
Other values (79) 581722
37.3%
Distinct63076
Distinct (%)85.2%
Missing53430
Missing (%)41.9%
Memory size996.0 KiB
2024-12-20T16:50:03.725373image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length43
Median length39
Mean length25.899769
Min length7

Characters and Unicode

Total characters1917593
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54526 ?
Unique (%)73.6%

Sample

1st rowЧадин Сергей Вячеславович
2nd rowИбраев Муйтен Ирекович
3rd rowБондаренко Вячеслав Александрович
4th rowКонаковКонстантинГеннадьевич
5th rowЗайцева Арина Евгеньевна
ValueCountFrequency (%)
александрович 5221
 
2.5%
сергеевич 4645
 
2.2%
александр 4148
 
2.0%
владимирович 3918
 
1.9%
сергей 3054
 
1.4%
дмитрий 2785
 
1.3%
николаевич 2594
 
1.2%
алексей 2552
 
1.2%
андрей 2359
 
1.1%
викторович 2027
 
1.0%
Other values (37991) 177891
84.2%
2024-12-20T16:50:04.615392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
а 175927
 
9.2%
и 174758
 
9.1%
е 151037
 
7.9%
в 143572
 
7.5%
137335
 
7.2%
о 128461
 
6.7%
н 121859
 
6.4%
р 101371
 
5.3%
л 94030
 
4.9%
к 60665
 
3.2%
Other values (57) 628578
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1917593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
а 175927
 
9.2%
и 174758
 
9.1%
е 151037
 
7.9%
в 143572
 
7.5%
137335
 
7.2%
о 128461
 
6.7%
н 121859
 
6.4%
р 101371
 
5.3%
л 94030
 
4.9%
к 60665
 
3.2%
Other values (57) 628578
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1917593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
а 175927
 
9.2%
и 174758
 
9.1%
е 151037
 
7.9%
в 143572
 
7.5%
137335
 
7.2%
о 128461
 
6.7%
н 121859
 
6.4%
р 101371
 
5.3%
л 94030
 
4.9%
к 60665
 
3.2%
Other values (57) 628578
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1917593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
а 175927
 
9.2%
и 174758
 
9.1%
е 151037
 
7.9%
в 143572
 
7.5%
137335
 
7.2%
о 128461
 
6.7%
н 121859
 
6.4%
р 101371
 
5.3%
л 94030
 
4.9%
к 60665
 
3.2%
Other values (57) 628578
32.8%
Distinct15459
Distinct (%)20.9%
Missing53430
Missing (%)41.9%
Memory size996.0 KiB
Minimum1874-02-08 00:00:00
Maximum2016-03-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-20T16:50:04.960876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:50:05.299485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2741
Distinct (%)92.7%
Missing124513
Missing (%)97.7%
Memory size996.0 KiB
2024-12-20T16:50:05.695675image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length35
Median length31
Mean length25.619418
Min length7

Characters and Unicode

Total characters75731
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2576 ?
Unique (%)87.1%

Sample

1st rowШведчиков Максим Александрович
2nd rowФридмане София Александровна
3rd rowСахарова Виктория Витальевна
4th rowНеудахина Ксения Алексеевна
5th rowЦикуновВиталийВладимирович
ValueCountFrequency (%)
александрович 143
 
1.9%
владимирович 124
 
1.7%
александр 119
 
1.6%
сергеевич 115
 
1.6%
сергей 109
 
1.5%
владимировна 96
 
1.3%
дмитрий 91
 
1.2%
александровна 82
 
1.1%
елена 79
 
1.1%
викторович 72
 
1.0%
Other values (3100) 6369
86.1%
2024-12-20T16:50:06.462907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
а 7760
 
10.2%
и 6609
 
8.7%
е 5861
 
7.7%
в 5704
 
7.5%
о 5221
 
6.9%
н 5143
 
6.8%
4458
 
5.9%
р 4065
 
5.4%
л 3733
 
4.9%
к 2324
 
3.1%
Other values (56) 24853
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
а 7760
 
10.2%
и 6609
 
8.7%
е 5861
 
7.7%
в 5704
 
7.5%
о 5221
 
6.9%
н 5143
 
6.8%
4458
 
5.9%
р 4065
 
5.4%
л 3733
 
4.9%
к 2324
 
3.1%
Other values (56) 24853
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
а 7760
 
10.2%
и 6609
 
8.7%
е 5861
 
7.7%
в 5704
 
7.5%
о 5221
 
6.9%
н 5143
 
6.8%
4458
 
5.9%
р 4065
 
5.4%
л 3733
 
4.9%
к 2324
 
3.1%
Other values (56) 24853
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
а 7760
 
10.2%
и 6609
 
8.7%
е 5861
 
7.7%
в 5704
 
7.5%
о 5221
 
6.9%
н 5143
 
6.8%
4458
 
5.9%
р 4065
 
5.4%
л 3733
 
4.9%
к 2324
 
3.1%
Other values (56) 24853
32.8%
Distinct1603
Distinct (%)1.3%
Missing110
Missing (%)0.1%
Memory size996.0 KiB
2024-12-20T16:50:07.021523image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.2306472
Min length2

Characters and Unicode

Total characters666170
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique329 ?
Unique (%)0.3%

Sample

1st row47.11
2nd row70.22
3rd row43.99
4th row47.19
5th row46.90
ValueCountFrequency (%)
41.20 10057
 
7.9%
46.90 7306
 
5.7%
47.91 5767
 
4.5%
49.41 5039
 
4.0%
43.29 4311
 
3.4%
46.73 4191
 
3.3%
62.01 4156
 
3.3%
43.21 4028
 
3.2%
73.11 2976
 
2.3%
43.39 2511
 
2.0%
Other values (1593) 77017
60.5%
2024-12-20T16:50:07.882648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 142279
21.4%
4 97366
14.6%
1 87453
13.1%
2 72093
10.8%
9 60552
9.1%
3 49860
 
7.5%
0 47145
 
7.1%
6 44000
 
6.6%
7 36499
 
5.5%
5 17719
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 666170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 142279
21.4%
4 97366
14.6%
1 87453
13.1%
2 72093
10.8%
9 60552
9.1%
3 49860
 
7.5%
0 47145
 
7.1%
6 44000
 
6.6%
7 36499
 
5.5%
5 17719
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 666170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 142279
21.4%
4 97366
14.6%
1 87453
13.1%
2 72093
10.8%
9 60552
9.1%
3 49860
 
7.5%
0 47145
 
7.1%
6 44000
 
6.6%
7 36499
 
5.5%
5 17719
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 666170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 142279
21.4%
4 97366
14.6%
1 87453
13.1%
2 72093
10.8%
9 60552
9.1%
3 49860
 
7.5%
0 47145
 
7.1%
6 44000
 
6.6%
7 36499
 
5.5%
5 17719
 
2.7%
Distinct297
Distinct (%)0.2%
Missing151
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean15.251135
Minimum0
Maximum996
Zeros61
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size996.0 KiB
2024-12-20T16:50:08.244625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median10
Q320
95-th percentile46
Maximum996
Range996
Interquartile range (IQR)16

Descriptive statistics

Standard deviation20.465876
Coefficient of variation (CV)1.3419248
Kurtosis229.09864
Mean15.251135
Median Absolute Deviation (MAD)7
Skewness9.3676944
Sum1941744
Variance418.85208
MonotonicityNot monotonic
2024-12-20T16:50:08.582048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15972
 
12.5%
2 7503
 
5.9%
4 6932
 
5.4%
3 6883
 
5.4%
5 6686
 
5.2%
6 4875
 
3.8%
7 4629
 
3.6%
8 4403
 
3.5%
9 4327
 
3.4%
10 4257
 
3.3%
Other values (287) 60851
47.7%
ValueCountFrequency (%)
0 61
 
< 0.1%
1 15972
12.5%
2 7503
5.9%
3 6883
5.4%
4 6932
5.4%
5 6686
5.2%
6 4875
 
3.8%
7 4629
 
3.6%
8 4403
 
3.5%
9 4327
 
3.4%
ValueCountFrequency (%)
996 1
< 0.1%
889 1
< 0.1%
888 1
< 0.1%
687 1
< 0.1%
624 1
< 0.1%
622 1
< 0.1%
617 2
< 0.1%
612 1
< 0.1%
608 1
< 0.1%
604 1
< 0.1%

Кол-во сотрудников
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct400
Distinct (%)1.7%
Missing104595
Missing (%)82.1%
Infinite0
Infinite (%)0.0%
Mean16.273848
Minimum0
Maximum13995
Zeros4807
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size996.0 KiB
2024-12-20T16:50:08.862391image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile20
Maximum13995
Range13995
Interquartile range (IQR)1

Descriptive statistics

Standard deviation191.54274
Coefficient of variation (CV)11.769972
Kurtosis1678.2446
Mean16.273848
Median Absolute Deviation (MAD)1
Skewness33.08067
Sum372248
Variance36688.622
MonotonicityNot monotonic
2024-12-20T16:50:09.180965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10231
 
8.0%
0 4807
 
3.8%
2 2166
 
1.7%
3 1291
 
1.0%
4 766
 
0.6%
5 570
 
0.4%
6 378
 
0.3%
7 277
 
0.2%
8 218
 
0.2%
9 206
 
0.2%
Other values (390) 1964
 
1.5%
(Missing) 104595
82.1%
ValueCountFrequency (%)
0 4807
3.8%
1 10231
8.0%
2 2166
 
1.7%
3 1291
 
1.0%
4 766
 
0.6%
5 570
 
0.4%
6 378
 
0.3%
7 277
 
0.2%
8 218
 
0.2%
9 206
 
0.2%
ValueCountFrequency (%)
13995 1
< 0.1%
8110 1
< 0.1%
7133 1
< 0.1%
5910 1
< 0.1%
5465 1
< 0.1%
4831 1
< 0.1%
4743 1
< 0.1%
4360 1
< 0.1%
4273 1
< 0.1%
4222 1
< 0.1%

Сайт
Text

Missing 

Distinct2852
Distinct (%)99.2%
Missing124594
Missing (%)97.7%
Memory size996.0 KiB
2024-12-20T16:50:09.613548image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length77
Median length46
Mean length13.24487
Min length5

Characters and Unicode

Total characters38079
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2833 ?
Unique (%)98.5%

Sample

1st roweltorgunet.ru
2nd rowcharme.su
3rd rowmcvetok.ru
4th rowwww.energomach.com
5th rowск-сип.рф
ValueCountFrequency (%)
www.fractalbio.com 3
 
0.1%
www.ohio8.vchecks.me 3
 
0.1%
www.rosneft.ru 3
 
0.1%
alpicagroup.ru 3
 
0.1%
alaris-stroy.ru 2
 
0.1%
пескоструйчелябинск.рф 2
 
0.1%
goodwill-ufa.ru 2
 
0.1%
volmetstroi.ru 2
 
0.1%
www.cnp_mo.ru 2
 
0.1%
armatura67.ru 2
 
0.1%
Other values (2850) 2859
99.2%
2024-12-20T16:50:10.431416image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 4314
 
11.3%
. 3401
 
8.9%
u 3165
 
8.3%
o 2083
 
5.5%
a 2067
 
5.4%
t 1856
 
4.9%
e 1844
 
4.8%
s 1754
 
4.6%
w 1456
 
3.8%
i 1423
 
3.7%
Other values (69) 14716
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38079
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 4314
 
11.3%
. 3401
 
8.9%
u 3165
 
8.3%
o 2083
 
5.5%
a 2067
 
5.4%
t 1856
 
4.9%
e 1844
 
4.8%
s 1754
 
4.6%
w 1456
 
3.8%
i 1423
 
3.7%
Other values (69) 14716
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38079
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 4314
 
11.3%
. 3401
 
8.9%
u 3165
 
8.3%
o 2083
 
5.5%
a 2067
 
5.4%
t 1856
 
4.9%
e 1844
 
4.8%
s 1754
 
4.6%
w 1456
 
3.8%
i 1423
 
3.7%
Other values (69) 14716
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38079
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 4314
 
11.3%
. 3401
 
8.9%
u 3165
 
8.3%
o 2083
 
5.5%
a 2067
 
5.4%
t 1856
 
4.9%
e 1844
 
4.8%
s 1754
 
4.6%
w 1456
 
3.8%
i 1423
 
3.7%
Other values (69) 14716
38.6%
Distinct116154
Distinct (%)91.3%
Missing289
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean7.9407234 × 1010
Minimum7.9 × 1010
Maximum8 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 KiB
2024-12-20T16:50:10.792223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum7.9 × 1010
5-th percentile7.9032086 × 1010
Q17.9137531 × 1010
median7.9272511 × 1010
Q37.9659296 × 1010
95-th percentile7.995657 × 1010
Maximum8 × 1010
Range1 × 109
Interquartile range (IQR)5.2176576 × 108

Descriptive statistics

Standard deviation3.1307906 × 108
Coefficient of variation (CV)0.003942702
Kurtosis-1.2220437
Mean7.9407234 × 1010
Median Absolute Deviation (MAD)2.244038 × 108
Skewness0.48439313
Sum1.0099012 × 1016
Variance9.80185 × 1016
MonotonicityNot monotonic
2024-12-20T16:50:11.119773image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.99959184 × 101038
 
< 0.1%
7.913456264 × 101023
 
< 0.1%
7.913763311 × 101013
 
< 0.1%
7.901282825 × 101013
 
< 0.1%
7.901693336 × 101013
 
< 0.1%
7.993600025 × 101012
 
< 0.1%
7.901680705 × 101012
 
< 0.1%
7.904543001 × 101010
 
< 0.1%
7.983462008 × 101010
 
< 0.1%
7.910699589 × 101010
 
< 0.1%
Other values (116144) 127026
99.7%
(Missing) 289
 
0.2%
ValueCountFrequency (%)
7.9 × 10102
< 0.1%
7.9 × 10101
< 0.1%
7.9 × 10101
< 0.1%
7.9 × 10101
< 0.1%
7.9 × 10101
< 0.1%
7.900000003 × 10101
< 0.1%
7.900000204 × 10101
< 0.1%
7.900001188 × 10101
< 0.1%
7.90000175 × 10101
< 0.1%
7.90000306 × 10101
< 0.1%
ValueCountFrequency (%)
8 × 10103
< 0.1%
8 × 10101
 
< 0.1%
7.999999999 × 10101
 
< 0.1%
7.999999999 × 10101
 
< 0.1%
7.999999996 × 10101
 
< 0.1%
7.999999996 × 10101
 
< 0.1%
7.99999997 × 10101
 
< 0.1%
7.999999968 × 10101
 
< 0.1%
7.999999937 × 10101
 
< 0.1%
7.999999869 × 10102
< 0.1%

Провайдер
Text

Missing 

Distinct61
Distinct (%)0.1%
Missing22042
Missing (%)17.3%
Memory size996.0 KiB
2024-12-20T16:50:11.400155image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length99
Median length40
Mean length20.552989
Min length8

Characters and Unicode

Total characters2166840
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowПАО "Мобильные ТелеСистемы"
2nd rowПАО "Мобильные ТелеСистемы"
3rd row"МГТС" ПАО
4th rowПАО "Мобильные ТелеСистемы"
5th rowООО "Т2 Мобайл"
ValueCountFrequency (%)
пао 77438
29.2%
мобильные 34213
12.9%
телесистемы 34129
12.9%
ооо 25276
 
9.5%
мегафон 22217
 
8.4%
мобайл 20263
 
7.6%
вымпел-коммуникации 18372
 
6.9%
т2 17641
 
6.6%
скартел 3911
 
1.5%
ао 2670
 
1.0%
Other values (53) 9441
 
3.6%
2024-12-20T16:50:12.309424image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 210836
 
9.7%
е 192911
 
8.9%
160144
 
7.4%
О 158627
 
7.3%
и 126666
 
5.8%
л 114128
 
5.3%
о 98045
 
4.5%
м 90211
 
4.2%
ы 86771
 
4.0%
А 81467
 
3.8%
Other values (46) 847034
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2166840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
" 210836
 
9.7%
е 192911
 
8.9%
160144
 
7.4%
О 158627
 
7.3%
и 126666
 
5.8%
л 114128
 
5.3%
о 98045
 
4.5%
м 90211
 
4.2%
ы 86771
 
4.0%
А 81467
 
3.8%
Other values (46) 847034
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2166840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
" 210836
 
9.7%
е 192911
 
8.9%
160144
 
7.4%
О 158627
 
7.3%
и 126666
 
5.8%
л 114128
 
5.3%
о 98045
 
4.5%
м 90211
 
4.2%
ы 86771
 
4.0%
А 81467
 
3.8%
Other values (46) 847034
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2166840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
" 210836
 
9.7%
е 192911
 
8.9%
160144
 
7.4%
О 158627
 
7.3%
и 126666
 
5.8%
л 114128
 
5.3%
о 98045
 
4.5%
м 90211
 
4.2%
ы 86771
 
4.0%
А 81467
 
3.8%
Other values (46) 847034
39.1%

Система налогообложения
Categorical

High correlation  Missing 

Distinct8
Distinct (%)< 0.1%
Missing53223
Missing (%)41.8%
Memory size996.0 KiB
ОСН
32286 
УСН 6%
27656 
УСН 15%
6072 
ПСН (только для ИП)
4227 
НПД
 
1846
Other values (3)
 
2159

Length

Max length19
Median length13
Mean length5.6173801
Min length3

Characters and Unicode

Total characters417068
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowУСН 6%
2nd rowУСН 6%
3rd rowУСН 15%
4th rowУСН 6%
5th rowУСН 6%

Common Values

ValueCountFrequency (%)
ОСН 32286
25.3%
УСН 6% 27656
21.7%
УСН 15% 6072
 
4.8%
ПСН (только для ИП) 4227
 
3.3%
НПД 1846
 
1.4%
УСН 6% + ПСН 1626
 
1.3%
УСН 15% + ПСН 475
 
0.4%
АУСН 58
 
< 0.1%
(Missing) 53223
41.8%

Length

2024-12-20T16:50:12.836641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T16:50:13.223508image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
усн 35829
28.2%
осн 32286
25.4%
6 29282
23.1%
15 6547
 
5.2%
псн 6328
 
5.0%
только 4227
 
3.3%
для 4227
 
3.3%
ип 4227
 
3.3%
2101
 
1.7%
нпд 1846
 
1.5%

Most occurring characters

ValueCountFrequency (%)
Н 76347
18.3%
С 74501
17.9%
52712
12.6%
У 35887
8.6%
% 35829
8.6%
О 32286
7.7%
6 29282
 
7.0%
П 12401
 
3.0%
о 8454
 
2.0%
л 8454
 
2.0%
Other values (13) 50915
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 417068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Н 76347
18.3%
С 74501
17.9%
52712
12.6%
У 35887
8.6%
% 35829
8.6%
О 32286
7.7%
6 29282
 
7.0%
П 12401
 
3.0%
о 8454
 
2.0%
л 8454
 
2.0%
Other values (13) 50915
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 417068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Н 76347
18.3%
С 74501
17.9%
52712
12.6%
У 35887
8.6%
% 35829
8.6%
О 32286
7.7%
6 29282
 
7.0%
П 12401
 
3.0%
о 8454
 
2.0%
л 8454
 
2.0%
Other values (13) 50915
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 417068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Н 76347
18.3%
С 74501
17.9%
52712
12.6%
У 35887
8.6%
% 35829
8.6%
О 32286
7.7%
6 29282
 
7.0%
П 12401
 
3.0%
о 8454
 
2.0%
л 8454
 
2.0%
Other values (13) 50915
12.2%
Distinct7289
Distinct (%)9.8%
Missing53131
Missing (%)41.7%
Memory size996.0 KiB
2024-12-20T16:50:13.861888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length237
Median length231
Mean length26.042414
Min length1

Characters and Unicode

Total characters1935941
Distinct characters138
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6053 ?
Unique (%)8.1%

Sample

1st rowКонсалтинг
2nd rowСтроительство и проектирование
3rd rowОптовая торговля
4th rowСтроительство и проектирование
5th rowСтроительство и проектирование
ValueCountFrequency (%)
и 29448
 
13.9%
строительство 20661
 
9.7%
проектирование 20532
 
9.7%
торговля 16148
 
7.6%
услуги 14293
 
6.7%
оптовая 9288
 
4.4%
розничная 6730
 
3.2%
розничные 6549
 
3.1%
обслуживание 6319
 
3.0%
средств 6058
 
2.9%
Other values (5065) 76339
35.9%
2024-12-20T16:50:15.128573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
о 200998
 
10.4%
т 169488
 
8.8%
и 164844
 
8.5%
145557
 
7.5%
р 141331
 
7.3%
е 128389
 
6.6%
н 111223
 
5.7%
а 105797
 
5.5%
в 92126
 
4.8%
с 88772
 
4.6%
Other values (128) 587416
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1935941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
о 200998
 
10.4%
т 169488
 
8.8%
и 164844
 
8.5%
145557
 
7.5%
р 141331
 
7.3%
е 128389
 
6.6%
н 111223
 
5.7%
а 105797
 
5.5%
в 92126
 
4.8%
с 88772
 
4.6%
Other values (128) 587416
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1935941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
о 200998
 
10.4%
т 169488
 
8.8%
и 164844
 
8.5%
145557
 
7.5%
р 141331
 
7.3%
е 128389
 
6.6%
н 111223
 
5.7%
а 105797
 
5.5%
в 92126
 
4.8%
с 88772
 
4.6%
Other values (128) 587416
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1935941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
о 200998
 
10.4%
т 169488
 
8.8%
и 164844
 
8.5%
145557
 
7.5%
р 141331
 
7.3%
е 128389
 
6.6%
н 111223
 
5.7%
а 105797
 
5.5%
в 92126
 
4.8%
с 88772
 
4.6%
Other values (128) 587416
30.3%
Distinct28
Distinct (%)0.3%
Missing117287
Missing (%)92.0%
Memory size996.0 KiB
Торговля
3162 
Строительство, недвижимость
1990 
Другое
1338 
Транспортные услуги и обслуживание транспортных средств
1050 
IT-услуги, разработка ПО
805 
Other values (23)
1837 

Length

Max length55
Median length53
Mean length20.551267
Min length6

Characters and Unicode

Total characters209253
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowТорговля
2nd rowПродажа/покупка/аренда недвижимости
3rd rowТорговля
4th rowКонсалтинг
5th rowСтроительство, недвижимость

Common Values

ValueCountFrequency (%)
Торговля 3162
 
2.5%
Строительство, недвижимость 1990
 
1.6%
Другое 1338
 
1.0%
Транспортные услуги и обслуживание транспортных средств 1050
 
0.8%
IT-услуги, разработка ПО 805
 
0.6%
Консалтинг 310
 
0.2%
Маркетинг, реклама, PR, продвижение в соцсетях, СМИ 228
 
0.2%
Грузоперевозки, логистика 225
 
0.2%
Бытовые услуги 176
 
0.1%
Производство, сельское хозяйство 137
 
0.1%
Other values (18) 761
 
0.6%
(Missing) 117287
92.0%

Length

2024-12-20T16:50:15.760163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
торговля 3164
14.5%
строительство 1991
 
9.1%
недвижимость 1990
 
9.1%
другое 1338
 
6.1%
услуги 1228
 
5.6%
и 1077
 
4.9%
транспортные 1050
 
4.8%
обслуживание 1050
 
4.8%
транспортных 1050
 
4.8%
средств 1050
 
4.8%
Other values (44) 6847
31.4%

Most occurring characters

ValueCountFrequency (%)
о 21308
 
10.2%
р 15579
 
7.4%
т 15545
 
7.4%
и 14329
 
6.8%
с 13914
 
6.6%
е 11936
 
5.7%
11653
 
5.6%
в 11092
 
5.3%
н 9736
 
4.7%
л 9654
 
4.6%
Other values (41) 74507
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 209253
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
о 21308
 
10.2%
р 15579
 
7.4%
т 15545
 
7.4%
и 14329
 
6.8%
с 13914
 
6.6%
е 11936
 
5.7%
11653
 
5.6%
в 11092
 
5.3%
н 9736
 
4.7%
л 9654
 
4.6%
Other values (41) 74507
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 209253
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
о 21308
 
10.2%
р 15579
 
7.4%
т 15545
 
7.4%
и 14329
 
6.8%
с 13914
 
6.6%
е 11936
 
5.7%
11653
 
5.6%
в 11092
 
5.3%
н 9736
 
4.7%
л 9654
 
4.6%
Other values (41) 74507
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 209253
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
о 21308
 
10.2%
р 15579
 
7.4%
т 15545
 
7.4%
и 14329
 
6.8%
с 13914
 
6.6%
е 11936
 
5.7%
11653
 
5.6%
в 11092
 
5.3%
н 9736
 
4.7%
л 9654
 
4.6%
Other values (41) 74507
35.6%

Кол-во сотрудников со слов клиента
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing53130
Missing (%)41.7%
Memory size996.0 KiB
0.0
66062 
3.0
 
4168
10.0
 
3183
11.0
 
926

Length

Max length4
Median length3
Mean length3.0552738
Min length3

Characters and Unicode

Total characters227126
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 66062
51.8%
3.0 4168
 
3.3%
10.0 3183
 
2.5%
11.0 926
 
0.7%
(Missing) 53130
41.7%

Length

2024-12-20T16:50:16.645469image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T16:50:16.876117image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 66062
88.9%
3.0 4168
 
5.6%
10.0 3183
 
4.3%
11.0 926
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 143584
63.2%
. 74339
32.7%
1 5035
 
2.2%
3 4168
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 227126
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 143584
63.2%
. 74339
32.7%
1 5035
 
2.2%
3 4168
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 227126
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 143584
63.2%
. 74339
32.7%
1 5035
 
2.2%
3 4168
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 227126
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 143584
63.2%
. 74339
32.7%
1 5035
 
2.2%
3 4168
 
1.8%
Distinct5
Distinct (%)< 0.1%
Missing60785
Missing (%)47.7%
Memory size996.0 KiB
600 000,00
45256 
3 000 000,00
17234 
10 000 000,00
 
3375
30 000 000,00
 
602
30 000 001,00
 
217

Length

Max length16
Median length13
Mean length13.705567
Min length13

Characters and Unicode

Total characters913942
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 3 000 000,00
2nd row 600 000,00
3rd row 600 000,00
4th row 3 000 000,00
5th row 600 000,00

Common Values

ValueCountFrequency (%)
600 000,00 45256
35.5%
3 000 000,00 17234
 
13.5%
10 000 000,00 3375
 
2.6%
30 000 000,00 602
 
0.5%
30 000 001,00 217
 
0.2%
(Missing) 60785
47.7%

Length

2024-12-20T16:50:17.146806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T16:50:17.382362image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
000,00 66467
42.9%
600 45256
29.2%
000 21428
 
13.8%
3 17234
 
11.1%
10 3375
 
2.2%
30 819
 
0.5%
001,00 217
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 492193
53.9%
200052
21.9%
  88112
 
9.6%
, 66684
 
7.3%
6 45256
 
5.0%
3 18053
 
2.0%
1 3592
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 913942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 492193
53.9%
200052
21.9%
  88112
 
9.6%
, 66684
 
7.3%
6 45256
 
5.0%
3 18053
 
2.0%
1 3592
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 913942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 492193
53.9%
200052
21.9%
  88112
 
9.6%
, 66684
 
7.3%
6 45256
 
5.0%
3 18053
 
2.0%
1 3592
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 913942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 492193
53.9%
200052
21.9%
  88112
 
9.6%
, 66684
 
7.3%
6 45256
 
5.0%
3 18053
 
2.0%
1 3592
 
0.4%
Distinct4
Distinct (%)< 0.1%
Missing53130
Missing (%)41.7%
Memory size996.0 KiB
-
39647 
100 000,00
19956 
500 000,00
12509 
500 001,00
 
2227

Length

Max length13
Median length6
Mean length9.2667106
Min length6

Characters and Unicode

Total characters688878
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -
2nd row -
3rd row -
4th row -
5th row 500 000,00

Common Values

ValueCountFrequency (%)
- 39647
31.1%
100 000,00 19956
 
15.7%
500 000,00 12509
 
9.8%
500 001,00 2227
 
1.7%
(Missing) 53130
41.7%

Length

2024-12-20T16:50:17.639485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T16:50:17.912378image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
39647
36.4%
000,00 32465
29.8%
100 19956
18.3%
500 14736
 
13.5%
001,00 2227
 
2.0%

Most occurring characters

ValueCountFrequency (%)
302311
43.9%
0 240617
34.9%
- 39647
 
5.8%
  34692
 
5.0%
, 34692
 
5.0%
1 22183
 
3.2%
5 14736
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 688878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
302311
43.9%
0 240617
34.9%
- 39647
 
5.8%
  34692
 
5.0%
, 34692
 
5.0%
1 22183
 
3.2%
5 14736
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 688878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
302311
43.9%
0 240617
34.9%
- 39647
 
5.8%
  34692
 
5.0%
, 34692
 
5.0%
1 22183
 
3.2%
5 14736
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 688878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
302311
43.9%
0 240617
34.9%
- 39647
 
5.8%
  34692
 
5.0%
, 34692
 
5.0%
1 22183
 
3.2%
5 14736
 
2.1%
Distinct12355
Distinct (%)80.6%
Missing112147
Missing (%)88.0%
Memory size996.0 KiB
2024-12-20T16:50:18.344971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length19
Median length17
Mean length11.349302
Min length6

Characters and Unicode

Total characters173894
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10764 ?
Unique (%)70.3%

Sample

1st row 22 350,00
2nd row 434 393,00
3rd row 21 932,00
4th row 7 012,00
5th row 4 251,00
ValueCountFrequency (%)
1 1023
 
3.5%
769
 
2.7%
2 723
 
2.5%
3 520
 
1.8%
4 505
 
1.8%
5 461
 
1.6%
6 385
 
1.3%
7 313
 
1.1%
9 307
 
1.1%
8 299
 
1.0%
Other values (1940) 23531
81.6%
2024-12-20T16:50:19.118210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47504
27.3%
0 35116
20.2%
, 14553
 
8.4%
  13514
 
7.8%
1 9982
 
5.7%
2 8073
 
4.6%
3 7116
 
4.1%
4 6808
 
3.9%
5 6595
 
3.8%
6 6108
 
3.5%
Other values (4) 18525
 
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 173894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
47504
27.3%
0 35116
20.2%
, 14553
 
8.4%
  13514
 
7.8%
1 9982
 
5.7%
2 8073
 
4.6%
3 7116
 
4.1%
4 6808
 
3.9%
5 6595
 
3.8%
6 6108
 
3.5%
Other values (4) 18525
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 173894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
47504
27.3%
0 35116
20.2%
, 14553
 
8.4%
  13514
 
7.8%
1 9982
 
5.7%
2 8073
 
4.6%
3 7116
 
4.1%
4 6808
 
3.9%
5 6595
 
3.8%
6 6108
 
3.5%
Other values (4) 18525
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 173894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
47504
27.3%
0 35116
20.2%
, 14553
 
8.4%
  13514
 
7.8%
1 9982
 
5.7%
2 8073
 
4.6%
3 7116
 
4.1%
4 6808
 
3.9%
5 6595
 
3.8%
6 6108
 
3.5%
Other values (4) 18525
 
10.7%

ЗСК
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing76369
Missing (%)59.9%
Memory size996.0 KiB
1.0
28299 
2.0
22801 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters153300
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 28299
 
22.2%
2.0 22801
 
17.9%
(Missing) 76369
59.9%

Length

2024-12-20T16:50:19.429641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T16:50:19.626733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 28299
55.4%
2.0 22801
44.6%

Most occurring characters

ValueCountFrequency (%)
. 51100
33.3%
0 51100
33.3%
1 28299
18.5%
2 22801
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 153300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 51100
33.3%
0 51100
33.3%
1 28299
18.5%
2 22801
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 153300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 51100
33.3%
0 51100
33.3%
1 28299
18.5%
2 22801
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 153300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 51100
33.3%
0 51100
33.3%
1 28299
18.5%
2 22801
14.9%

Негативная информация
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing93228
Missing (%)73.1%
Memory size996.0 KiB
Имеется
34241 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters239687
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowИмеется
2nd rowИмеется
3rd rowИмеется
4th rowИмеется
5th rowИмеется

Common Values

ValueCountFrequency (%)
Имеется 34241
 
26.9%
(Missing) 93228
73.1%

Length

2024-12-20T16:50:19.850847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T16:50:20.084131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
имеется 34241
100.0%

Most occurring characters

ValueCountFrequency (%)
е 68482
28.6%
И 34241
14.3%
м 34241
14.3%
т 34241
14.3%
с 34241
14.3%
я 34241
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239687
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
е 68482
28.6%
И 34241
14.3%
м 34241
14.3%
т 34241
14.3%
с 34241
14.3%
я 34241
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239687
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
е 68482
28.6%
И 34241
14.3%
м 34241
14.3%
т 34241
14.3%
с 34241
14.3%
я 34241
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239687
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
е 68482
28.6%
И 34241
14.3%
м 34241
14.3%
т 34241
14.3%
с 34241
14.3%
я 34241
14.3%

Негатив относительно ГД
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing111296
Missing (%)87.3%
Memory size996.0 KiB
Имеется
16173 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters113211
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowИмеется
2nd rowИмеется
3rd rowИмеется
4th rowИмеется
5th rowИмеется

Common Values

ValueCountFrequency (%)
Имеется 16173
 
12.7%
(Missing) 111296
87.3%

Length

2024-12-20T16:50:20.296791image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T16:50:20.512452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
имеется 16173
100.0%

Most occurring characters

ValueCountFrequency (%)
е 32346
28.6%
И 16173
14.3%
м 16173
14.3%
т 16173
14.3%
с 16173
14.3%
я 16173
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113211
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
е 32346
28.6%
И 16173
14.3%
м 16173
14.3%
т 16173
14.3%
с 16173
14.3%
я 16173
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113211
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
е 32346
28.6%
И 16173
14.3%
м 16173
14.3%
т 16173
14.3%
с 16173
14.3%
я 16173
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113211
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
е 32346
28.6%
И 16173
14.3%
м 16173
14.3%
т 16173
14.3%
с 16173
14.3%
я 16173
14.3%

Мошенники
Text

Missing 

Distinct27302
Distinct (%)45.5%
Missing67434
Missing (%)52.9%
Memory size996.0 KiB
2024-12-20T16:50:20.844299image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length14
Median length13
Mean length7.6636962
Min length1

Characters and Unicode

Total characters460090
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26319 ?
Unique (%)43.8%

Sample

1st row0,03022038916
2nd row0,5324924146
3rd row0,1527235638
4th row0,02585764304
5th row0,03859294933
ValueCountFrequency (%)
0 24122
40.2%
0,5288064437 6098
 
10.2%
0,8239536693 71
 
0.1%
0,3706047949 67
 
0.1%
0,5293610462 62
 
0.1%
0,8332915944 51
 
0.1%
0,3987571641 49
 
0.1%
0,5003282471 38
 
0.1%
0,8768521502 36
 
0.1%
0,5324924146 36
 
0.1%
Other values (27292) 29405
49.0%
2024-12-20T16:50:21.551234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 98034
21.3%
4 42077
9.1%
8 41754
9.1%
2 37268
 
8.1%
3 36819
 
8.0%
, 35913
 
7.8%
6 35773
 
7.8%
5 35749
 
7.8%
7 34731
 
7.5%
1 31613
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 460090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 98034
21.3%
4 42077
9.1%
8 41754
9.1%
2 37268
 
8.1%
3 36819
 
8.0%
, 35913
 
7.8%
6 35773
 
7.8%
5 35749
 
7.8%
7 34731
 
7.5%
1 31613
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 460090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 98034
21.3%
4 42077
9.1%
8 41754
9.1%
2 37268
 
8.1%
3 36819
 
8.0%
, 35913
 
7.8%
6 35773
 
7.8%
5 35749
 
7.8%
7 34731
 
7.5%
1 31613
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 460090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 98034
21.3%
4 42077
9.1%
8 41754
9.1%
2 37268
 
8.1%
3 36819
 
8.0%
, 35913
 
7.8%
6 35773
 
7.8%
5 35749
 
7.8%
7 34731
 
7.5%
1 31613
 
6.9%

Сервисы регистраторы
Real number (ℝ)

Missing  Zeros 

Distinct28450
Distinct (%)47.4%
Missing67434
Missing (%)52.9%
Infinite0
Infinite (%)0.0%
Mean0.35886035
Minimum0
Maximum0.99958655
Zeros24122
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size996.0 KiB
2024-12-20T16:50:21.910031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.067403557
Q30.92654455
95-th percentile0.99306501
Maximum0.99958655
Range0.99958655
Interquartile range (IQR)0.92654455

Descriptive statistics

Standard deviation0.42883178
Coefficient of variation (CV)1.1949823
Kurtosis-1.515192
Mean0.35886035
Median Absolute Deviation (MAD)0.067403557
Skewness0.58972604
Sum21544.181
Variance0.18389669
MonotonicityNot monotonic
2024-12-20T16:50:22.252415image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24122
 
18.9%
0.9930650147 6331
 
5.0%
0.9818980099 122
 
0.1%
0.984543331 45
 
< 0.1%
0.9825779968 31
 
< 0.1%
0.9637479504 27
 
< 0.1%
0.9819423107 25
 
< 0.1%
0.9862911311 21
 
< 0.1%
0.9836870384 20
 
< 0.1%
0.9822528555 19
 
< 0.1%
Other values (28440) 29272
23.0%
(Missing) 67434
52.9%
ValueCountFrequency (%)
0 24122
18.9%
0.001717721629 1
 
< 0.1%
0.001832505842 1
 
< 0.1%
0.002260230643 1
 
< 0.1%
0.002540018051 1
 
< 0.1%
0.002669328806 1
 
< 0.1%
0.002672524768 1
 
< 0.1%
0.002937233084 1
 
< 0.1%
0.00306588795 1
 
< 0.1%
0.003457637667 1
 
< 0.1%
ValueCountFrequency (%)
0.9995865538 1
< 0.1%
0.9994414415 1
< 0.1%
0.9994399598 1
< 0.1%
0.999439122 1
< 0.1%
0.9993987133 1
< 0.1%
0.9993872172 1
< 0.1%
0.999369834 1
< 0.1%
0.9993574462 1
< 0.1%
0.9993465912 1
< 0.1%
0.9992612934 1
< 0.1%
Distinct6
Distinct (%)< 0.1%
Missing67434
Missing (%)52.9%
Infinite0
Infinite (%)0.0%
Mean1.4378946
Minimum0
Maximum5
Zeros39153
Zeros (%)30.7%
Negative0
Negative (%)0.0%
Memory size996.0 KiB
2024-12-20T16:50:22.503725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1100861
Coefficient of variation (CV)1.4674832
Kurtosis-1.0273394
Mean1.4378946
Median Absolute Deviation (MAD)0
Skewness0.9132135
Sum86324
Variance4.4524634
MonotonicityNot monotonic
2024-12-20T16:50:22.726399image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 39153
30.7%
5 12568
 
9.9%
4 3446
 
2.7%
2 2108
 
1.7%
1 1398
 
1.1%
3 1362
 
1.1%
(Missing) 67434
52.9%
ValueCountFrequency (%)
0 39153
30.7%
1 1398
 
1.1%
2 2108
 
1.7%
3 1362
 
1.1%
4 3446
 
2.7%
5 12568
 
9.9%
ValueCountFrequency (%)
5 12568
 
9.9%
4 3446
 
2.7%
3 1362
 
1.1%
2 2108
 
1.7%
1 1398
 
1.1%
0 39153
30.7%
Distinct3566
Distinct (%)5.9%
Missing67434
Missing (%)52.9%
Infinite0
Infinite (%)0.0%
Mean227.60272
Minimum0
Maximum6691
Zeros41349
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size996.0 KiB
2024-12-20T16:50:22.990134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile1615.3
Maximum6691
Range6691
Interquartile range (IQR)20

Descriptive statistics

Standard deviation799.17117
Coefficient of variation (CV)3.511255
Kurtosis22.602202
Mean227.60272
Median Absolute Deviation (MAD)0
Skewness4.6042054
Sum13664129
Variance638674.56
MonotonicityNot monotonic
2024-12-20T16:50:23.339279image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41349
32.4%
2 516
 
0.4%
3 428
 
0.3%
4 396
 
0.3%
5 273
 
0.2%
1 204
 
0.2%
6 176
 
0.1%
11 162
 
0.1%
10 159
 
0.1%
7 159
 
0.1%
Other values (3556) 16213
 
12.7%
(Missing) 67434
52.9%
ValueCountFrequency (%)
0 41349
32.4%
1 204
 
0.2%
2 516
 
0.4%
3 428
 
0.3%
4 396
 
0.3%
5 273
 
0.2%
6 176
 
0.1%
7 159
 
0.1%
8 138
 
0.1%
9 145
 
0.1%
ValueCountFrequency (%)
6691 1
< 0.1%
6689 1
< 0.1%
6627 1
< 0.1%
6599 1
< 0.1%
6562 1
< 0.1%
6549 1
< 0.1%
6540 1
< 0.1%
6522 1
< 0.1%
6520 1
< 0.1%
6470 1
< 0.1%
Distinct4336
Distinct (%)7.2%
Missing67434
Missing (%)52.9%
Infinite0
Infinite (%)0.0%
Mean335.44792
Minimum0
Maximum6918
Zeros40944
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size996.0 KiB
2024-12-20T16:50:23.650302image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q350
95-th percentile2779
Maximum6918
Range6918
Interquartile range (IQR)50

Descriptive statistics

Standard deviation1008.2269
Coefficient of variation (CV)3.0056137
Kurtosis13.171055
Mean335.44792
Median Absolute Deviation (MAD)0
Skewness3.6192311
Sum20138616
Variance1016521.4
MonotonicityNot monotonic
2024-12-20T16:50:23.973675image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 40944
32.1%
28 146
 
0.1%
35 145
 
0.1%
56 133
 
0.1%
41 126
 
0.1%
29 125
 
0.1%
50 125
 
0.1%
21 122
 
0.1%
42 120
 
0.1%
57 120
 
0.1%
Other values (4326) 17929
 
14.1%
(Missing) 67434
52.9%
ValueCountFrequency (%)
0 40944
32.1%
1 42
 
< 0.1%
2 33
 
< 0.1%
3 29
 
< 0.1%
4 33
 
< 0.1%
5 34
 
< 0.1%
6 60
 
< 0.1%
7 79
 
0.1%
8 47
 
< 0.1%
9 43
 
< 0.1%
ValueCountFrequency (%)
6918 1
< 0.1%
6882 1
< 0.1%
6837 1
< 0.1%
6812 1
< 0.1%
6797 1
< 0.1%
6787 1
< 0.1%
6785 1
< 0.1%
6762 1
< 0.1%
6761 1
< 0.1%
6724 1
< 0.1%

Налоговая нагрузка
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size996.0 KiB
-
60785 
384 000,00
45256 
1 920 000,00
17234 
6 400 000,00
 
3375
19 200 000,00
 
602

Length

Max length18
Median length17
Mean length12.004605
Min length8

Characters and Unicode

Total characters1530215
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -
2nd row 1 920 000,00
3rd row 384 000,00
4th row -
5th row 384 000,00

Common Values

ValueCountFrequency (%)
- 60785
47.7%
384 000,00 45256
35.5%
1 920 000,00 17234
 
13.5%
6 400 000,00 3375
 
2.6%
19 200 000,00 602
 
0.5%
19 200 000,64 217
 
0.2%

Length

2024-12-20T16:50:24.351205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T16:50:24.645036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
000,00 66467
30.8%
60785
28.2%
384 45256
21.0%
1 17234
 
8.0%
920 17234
 
8.0%
6 3375
 
1.6%
400 3375
 
1.6%
19 819
 
0.4%
200 819
 
0.4%
000,64 217
 
0.1%

Most occurring characters

ValueCountFrequency (%)
758915
49.6%
0 358608
23.4%
  88112
 
5.8%
, 66684
 
4.4%
- 60785
 
4.0%
4 48848
 
3.2%
3 45256
 
3.0%
8 45256
 
3.0%
1 18053
 
1.2%
9 18053
 
1.2%
Other values (2) 21645
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1530215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
758915
49.6%
0 358608
23.4%
  88112
 
5.8%
, 66684
 
4.4%
- 60785
 
4.0%
4 48848
 
3.2%
3 45256
 
3.0%
8 45256
 
3.0%
1 18053
 
1.2%
9 18053
 
1.2%
Other values (2) 21645
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1530215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
758915
49.6%
0 358608
23.4%
  88112
 
5.8%
, 66684
 
4.4%
- 60785
 
4.0%
4 48848
 
3.2%
3 45256
 
3.0%
8 45256
 
3.0%
1 18053
 
1.2%
9 18053
 
1.2%
Other values (2) 21645
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1530215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
758915
49.6%
0 358608
23.4%
  88112
 
5.8%
, 66684
 
4.4%
- 60785
 
4.0%
4 48848
 
3.2%
3 45256
 
3.0%
8 45256
 
3.0%
1 18053
 
1.2%
9 18053
 
1.2%
Other values (2) 21645
 
1.4%

Interactions

2024-12-20T16:49:50.582461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:33.608623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:35.627296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:37.986992image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:40.178203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:43.281268image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:46.291894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:48.277106image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:50.838705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:33.873501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:36.086520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:38.226704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:40.414943image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:43.668427image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:46.542860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:48.549020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:51.091695image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:34.108916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:36.297800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:38.450506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:40.669697image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:44.026451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:46.774807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:48.788877image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:51.357286image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:34.360363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:36.539838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:38.717156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:41.101170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:44.394154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:47.021344image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:49.268054image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:51.635722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:34.627057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:36.971961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:38.974856image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:41.464292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:44.783686image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:47.260396image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:49.536239image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:51.889876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:34.875854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:37.224881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:39.211244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:42.142200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:45.119750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:47.516015image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:49.793323image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:52.140018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:35.121286image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:37.459133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:39.445892image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:42.507725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:45.522071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:47.761916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:50.043244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:52.401705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:35.367913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:37.713367image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:39.887257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:42.887346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:45.925735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:48.022725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-20T16:49:50.294885image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-20T16:50:24.885365image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Планируемый оборот по анкете (руб)Планируемый оборот по снятию д/с (руб)Срок жизни SIM-карты/номера (от даты замены e/SIM-карты)Деятельность клиента со слов клиентаЗСКИННКол-во дополнительных ОКВЭДОВКол-во сотрудниковКол-во сотрудников со слов клиентаНалоговая нагрузкаНомер телефонаСервисы регистраторыСистема налогообложенияСрок жизни SIM в текущем пользовательском устройствеСрок жизни SIM-карты/номера (количество дней/часов/минут, которое прошло от даты заключения договора)
Планируемый оборот по анкете (руб)1.0000.2160.0490.1670.1450.0820.0241.0000.1861.0000.0440.0680.0870.0240.036
Планируемый оборот по снятию д/с (руб)0.2161.0000.0570.0980.0710.0440.0071.0000.0590.2090.0260.0560.0710.0170.021
Срок жизни SIM-карты/номера (от даты замены e/SIM-карты)0.0490.0571.0000.0750.2280.154-0.114-0.0420.0210.090-0.0170.0960.1440.1070.113
Деятельность клиента со слов клиента0.1670.0980.0751.0000.1570.0510.0281.0000.0790.1280.0240.0630.1440.0000.024
ЗСК0.1450.0710.2280.1571.0000.0780.0261.0000.1360.1580.0650.1800.2010.1100.131
ИНН0.0820.0440.1540.0510.0781.000-0.2830.0020.0850.0630.001-0.0420.228-0.028-0.020
Кол-во дополнительных ОКВЭДОВ0.0240.007-0.1140.0280.026-0.2831.000-0.0120.0100.016-0.011-0.1240.0110.0070.005
Кол-во сотрудников1.0001.000-0.0421.0001.0000.002-0.0121.0001.0000.007-0.016-0.1931.0000.0040.004
Кол-во сотрудников со слов клиента0.1860.0590.0210.0790.1360.0850.0101.0001.0000.1790.0290.0710.0730.0210.025
Налоговая нагрузка1.0000.2090.0900.1280.1580.0630.0160.0070.1791.0000.0390.0870.1090.0300.047
Номер телефона0.0440.026-0.0170.0240.0650.001-0.011-0.0160.0290.0391.0000.0390.039-0.042-0.047
Сервисы регистраторы0.0680.0560.0960.0630.180-0.042-0.124-0.1930.0710.0870.0391.0000.1050.3700.376
Система налогообложения0.0870.0710.1440.1440.2010.2280.0111.0000.0730.1090.0390.1051.0000.0520.057
Срок жизни SIM в текущем пользовательском устройстве0.0240.0170.1070.0000.110-0.0280.0070.0040.0210.030-0.0420.3700.0521.0000.979
Срок жизни SIM-карты/номера (количество дней/часов/минут, которое прошло от даты заключения договора)0.0360.0210.1130.0240.131-0.0200.0050.0040.0250.047-0.0470.3760.0570.9791.000

Missing values

2024-12-20T16:49:52.934721image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-20T16:49:54.102616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-20T16:49:56.368569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ИННДата регистрацииУставной капитал (руб)АдресФИО Генерального директораДата рождения Генерального директораФИО БенефициараОсновной ОКВЭДКол-во дополнительных ОКВЭДОВКол-во сотрудниковСайтНомер телефонаПровайдерСистема налогообложенияДеятельность клиентаДеятельность клиента со слов клиентаКол-во сотрудников со слов клиентаПланируемый оборот по анкете (руб)Планируемый оборот по снятию д/с (руб)Доходы (тыс, руб.)ЗСКНегативная информацияНегатив относительно ГДМошенникиСервисы регистраторыСрок жизни SIM-карты/номера (от даты замены e/SIM-карты)Срок жизни SIM в текущем пользовательском устройствеСрок жизни SIM-карты/номера (количество дней/часов/минут, которое прошло от даты заключения договора)Налоговая нагрузка
06205000124128/12/2022NaNNaNNaNNaNNaN47.118.0NaNNaN7.915627e+10ПАО "Мобильные ТелеСистемы"NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-
14703075316954/15/2021NaNг. МоскваЧадин Сергей Вячеславович2/11/1979NaN70.2221.0NaNNaN7.963728e+10NaNУСН 6%КонсалтингNaN0.03 000 000,00-NaNNaNNaNИмеется0,030220389160.0605755.00.00.01 920 000,00
2273625875793/16/2023NaNг. УфаИбраев Муйтен Ирекович7/2/2003NaN43.991.0NaNNaN7.987246e+10ПАО "Мобильные ТелеСистемы"УСН 6%Строительство и проектированиеNaN0.0600 000,00-NaNNaNNaNNaN0,53249241460.9930650.02.049.0384 000,00
37735718213858/11/2022NaNNaNNaNNaNNaN47.195.0NaNNaN7.985363e+10"МГТС" ПАОNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-
43001147740454/18/2022NaNАстраханская областьБондаренко Вячеслав Александрович6/20/1978NaN46.9014.0NaNNaN7.980143e+10ПАО "Мобильные ТелеСистемы"УСН 15%Оптовая торговляNaN0.0600 000,00-NaN2.0NaNNaNNaNNaNNaNNaNNaN384 000,00
550010320330211/25/2021NaNг. МоскваКонаковКонстантинГеннадьевич4/8/1978NaN74.905.0NaNNaN7.916800e+10NaNУСН 6%Строительство и проектированиеNaN0.03 000 000,00-NaNNaNNaNNaN0,15272356380.1614215.00.00.01 920 000,00
67404116939024/18/2023NaNЧелябинская областьЗайцева Арина Евгеньевна4/17/2000NaN41.2051.0NaNNaN7.951245e+10ООО "Т2 Мобайл"УСН 6%Строительство и проектированиеNaN0.0600 000,00500 000,00NaNNaNNaNNaN0,025857643040.0712035.00.00.0384 000,00
73106113068932/16/2024NaNNaNNaNNaNNaN49.416.0NaNNaN7.929005e+10ПАО "МЕГАФОН"NaNNaNNaNNaNNaNNaNNaNNaNИмеетсяNaNNaNNaNNaNNaNNaN-
897240861906/20/202210 000,00г. МоскваБунин Андрей Владимирович5/13/1971NaN45.325.0NaNNaN7.925670e+10ПАО "МегаФон"ОСНРозничная торговляNaN0.0600 000,00-NaN2.0NaNNaNNaNNaNNaNNaNNaN384 000,00
9970703904810/26/2024100 000,00г. МоскваNaNNaNNaN52.2424.0NaNNaN7.922229e+10ПАО "МЕГАФОН"NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-
ИННДата регистрацииУставной капитал (руб)АдресФИО Генерального директораДата рождения Генерального директораФИО БенефициараОсновной ОКВЭДКол-во дополнительных ОКВЭДОВКол-во сотрудниковСайтНомер телефонаПровайдерСистема налогообложенияДеятельность клиентаДеятельность клиента со слов клиентаКол-во сотрудников со слов клиентаПланируемый оборот по анкете (руб)Планируемый оборот по снятию д/с (руб)Доходы (тыс, руб.)ЗСКНегативная информацияНегатив относительно ГДМошенникиСервисы регистраторыСрок жизни SIM-карты/номера (от даты замены e/SIM-карты)Срок жизни SIM в текущем пользовательском устройствеСрок жизни SIM-карты/номера (количество дней/часов/минут, которое прошло от даты заключения договора)Налоговая нагрузка
1274596165082662054/28/2023NaNNaNNaNNaNNaN47.6513.0NaNNaN7.952557e+10ООО "Т2 Мобайл"NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-
127460420539051011/6/202110 000,00г КемеровоNaNNaNNaN72.1916.0NaNNaN7.983220e+10ПАО "Мобильные ТелеСистемы"NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-
1274616617109303677/3/2024NaNNaNNaNNaNNaN68.2010.0NaNNaN7.992331e+10ООО "Т2 Мобайл"NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-
127462258042310632/20/2023NaNNaNNaNNaNNaN46.7113.0NaNNaN7.961367e+10ПАО "Вымпел-Коммуникации"NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-
12746352080259562312/15/2021NaNНижегородская областьАзимова Гюлюш Мабуд Кызы6/15/1960NaN49.411.0NaNNaN7.950616e+10ООО "Т2 Мобайл"УСН 6%Розничная торговляNaN0.0600 000,00-NaNNaNNaNNaN0,043553645490.2548774.00.00.0384 000,00
12746477223134219/21/202212 500,00г. СмоленскКунаев Сергей Александрович10/30/1982Кунаева Галина Сергеевна46.49.3369.01.0NaN7.996629e+10ООО "Скартел"ОСНОптовая торговляNaN3.03 000 000,00-109 854,001.0NaNNaN00.0000000.00.00.01 920 000,00
127465540422176610/4/202150 000,00г. НовосибирскСемкина Людмила Анатольевна3/17/1974NaN46.4922.0NaNNaN7.983136e+10NaNОСНОптовая торговляNaN0.0600 000,00-NaN1.0NaNИмеется0,52880644370.9930650.055.058.0384 000,00
12746697031064709/14/202250 000,00г. МоскваNaNNaNNaN46.9013.0NaNNaN7.999677e+10ООО "Скартел"NaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaN-
12746761631558053/24/202210 500,00г. Ростов-на-ДонуРоговойАндрейОлегович10/7/1998СТЕПАНЕНКОВЕРОНИКАИГОРЕВНА46.90150.01.0NaN7.906415e+10ПАО "Вымпел-Коммуникации"ОСНОптовая торговляNaN0.0600 000,00-NaN1.0ИмеетсяNaN00.0000000.00.00.0384 000,00
12746897240672532/1/202210 000,00г. МоскваNaNNaNNaN46.732.0NaNNaN7.981449e+10NaNNaNNaNNaNNaNNaNNaNNaN2.0NaNNaNNaNNaNNaNNaNNaN-

Duplicate rows

Most frequently occurring

ИННДата регистрацииУставной капитал (руб)АдресФИО Генерального директораДата рождения Генерального директораФИО БенефициараОсновной ОКВЭДКол-во дополнительных ОКВЭДОВКол-во сотрудниковСайтНомер телефонаПровайдерСистема налогообложенияДеятельность клиентаДеятельность клиента со слов клиентаКол-во сотрудников со слов клиентаПланируемый оборот по анкете (руб)Планируемый оборот по снятию д/с (руб)Доходы (тыс, руб.)ЗСКНегативная информацияНегатив относительно ГДМошенникиСервисы регистраторыСрок жизни SIM-карты/номера (от даты замены e/SIM-карты)Срок жизни SIM в текущем пользовательском устройствеСрок жизни SIM-карты/номера (количество дней/часов/минут, которое прошло от даты заключения договора)Налоговая нагрузка# duplicates
03000175989/28/202310 000,00Бурятия Респ, г.о. город Улан-Удэ, г Улан-Удэ, б-р Карла Маркса, д. 9, кв. 57NaNNaNNaN47.111.0NaNNaN7.964406e+10ПАО "Вымпел-Коммуникации"NaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaN-2
136280123248/27/202410 000,00Воронежская облNaNNaNNaN73.118.01.0NaN7.900300e+10ПАО "Вымпел-Коммуникации"NaNNaNNaNNaNNaNNaN11 534,00NaNNaNNaN00.00.00.00.0-2
248251417156/22/202210 000,00г ЛипецкNaNNaNNaN43.3120.03.0NaN7.961602e+10ПАО "Вымпел-Коммуникации"NaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaN-2
366711032206/24/202210 000,00г. ЕкатеринбургNaNNaNNaN93.2918.03.0NaN7.909016e+10ПАО "Вымпел-Коммуникации"NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-2
477264772055/20/202210 000,00г. МоскваNaNNaNNaN46.4469.01.0NaN7.903578e+10ПАО "Мобильные ТелеСистемы"NaNNaNNaNNaNNaNNaN95 690,001.0ИмеетсяNaNNaNNaNNaNNaNNaN-2
597260477355/28/2024100 000,00г. МоскваNaNNaNNaN49.4115.01.0NaN7.925034e+10ПАО "МегаФон"NaNNaNNaNNaNNaNNaN50 858,001.0ИмеетсяNaNNaNNaNNaNNaNNaN-2
6276077335728/12/2024NaNNaNNaNNaNNaN41.2047.0NaNNaN7.965660e+10ПАО "ВЫМПЕЛКОМ"NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-2
716560894051210/21/2024NaNNaNNaNNaNNaN47.9117.0NaNNaN7.904764e+10ПАО "Вымпел-Коммуникации"NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-2
859206146114812/18/2023NaNNaNNaNNaNNaN47.2412.0NaNNaN7.958142e+10ООО "Т2 Мобайл"NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-2
966280314292410/28/2021NaNNaNNaNNaNNaN41.2083.0NaNNaN7.902587e+10ООО "Т2 Мобайл"NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-2